code: def sample_input(batch_size=15, max_length=1000):
      generator = torch.Generator()
      generator.manual_seed(0)
  
      lengths = torch.randint(
          max_length // 4,
          max_length - 7,
          (batch_size,),
          generator=generator,
      )
      input_spectrograms = torch.randn(
          (batch_size, 80, 1000), generator=generator
      )
  
      input_spectrograms = mask_sequence_tensor(
          input_spectrograms, lengths
      )
  
      return input_spectrograms, lengths
  
file_path: NeMo/tests/collections/tts/test_spectrogram_enhancer.py
imports: import pytest
  import torch
  from einops import rearrange
  from omegaconf import DictConfig
  from nemo.collections.tts.models import SpectrogramEnhancerModel
  from nemo.collections.tts.parts.utils.helpers import mask_sequence_tensor
label_desc: Use image node_id 4 for calling a global function with example usage: sample_input(batch_size, max_length) and returns: input_spectrograms,  lengths